With the evolution of modern data communications networks, vast amounts of digital content can now be readily transferred amongst end users, media content providers, and network service providers, at relatively high data transfer rates at almost any location. Whether digital content distribution occurs over wireline networks, such as fiber-optic or cable networks, or over wireless networks, such as 3 G, 3GPP LTE, LTE Advanced, or 4 G cellular networks, the task of increasing communications service capability and maximizing the utilization of existing network communications resources remains a key objective for most network service providers.
Over the past decade, consumer exposure to state-of-the-art digital media content distribution and playback technologies (e.g., tablet computers, netbooks, multi-function cellular phones, PDAs, electronic-book devices, etc.) has created a significant demand for improved digital content delivery capability, and most service providers have struggled to provide sufficient communications infrastructure to keep up with this growing consumer demand. Presently, there are many different types of data communications networks available that can function independently (e.g., as Local Area Networks or LANs) or collectively as part of a group of interconnected networks (e.g., Wide Area Networks or WANs), such as the World Wide Web. Some of these networks include technologies that facilitate relatively fast, high data rate transmissions (e.g., Fiber-optic, Cable, and Digital Subscriber Line (DSL) networks), while others can only facilitate much slower data rate transmissions (e.g., 3 G cellular networks). Regardless of a network's type, topology, or employed technologies, almost all modern-day networks are susceptible to congestion or degradation due to a high demand for transferring an alarming amount of digital content between and amongst various network nodes.
As would be understood by those skilled in the art, network congestion generally refers to a state of data transfer overload (a load that burdens network capacity) between links in a data communications network. These heavy loads typically degrade a network's Quality of Service (QOS) and user's Quality of Experience (QOE). Some negative effects of network congestion, affecting QOS/QOE, may include queuing delay, packet loss, and the blocking of new and existing connections.
Mobile broadband services are also becoming very popular in modern society, where almost every teenager and adult in the U.S. owns at least one wireless communications device (e.g., a cellular phone or PDA). These services can provide a way for individuals to stay connected to the Internet while operating within and roaming between various wireless coverage areas. A concurrent trend is the huge increase in applications and media content distribution services that can facilitate the delivery of large, burdensome media content files to or from user equipment. Large media content file transfers have the signature feature of consuming significant amounts of network resources (i.e., channel bandwidth) over extended periods of time. Methods of enabling and making this particular data type delivery more efficient are very important to end users and service providers alike. The processes facilitating more efficient media content delivery are particularly relevant for wireless networks that have limited bandwidth resources.
Most wireless networks operate using shared communications channels where concurrent, competing requests for channel access is commonplace. In these networks, data transfers can be slowed or degraded during periods of network channel congestion (e.g., during periods of heavy network traffic) or during times when an end user is positioned in an area with relatively poor radio coverage or radio communications quality (e.g., in areas with physical or radio communications interference sources). Each of these problems can negatively impact network communications for an end user, however, congestion tends to more significantly impact a network service provider's QOS as well as the QOE encountered by its collective users. Accordingly, it would be advantageous to be able to distinguish between the two sources of network communications deficiency, by accurately determining if the cause of decreased communications throughput was due to a state of network congestion, a state or reduced radio communications quality, or both.
In general, during a state of network congestion, it would be beneficial to be able to adaptively throttle a large media content delivery session, by adjusting its data delivery rate. This would prevent further congesting a network during periods of network resource exhaustion. By selectively choosing network data delivery times and data transfer rates, providers could effectively utilize network resources when surplus network bandwidth exists, as opposed to allowing a large media content file delivery to compete with unrelated cross traffic during periods of peak network resource use.
Accordingly, it would be beneficial to have improved systems and methods for data content delivery that could distinguish between network congestion and network link quality (e.g., link quality in the presence of one or more interference sources). This distinction is necessary, because when a wireless communications channel is operating at capacity, large media content file transfers may need to be slowed to avoid negatively impacting unrelated cross traffic that is concurrently sharing the same communications channel. In contrast, when a wireless communications channel is not operating at capacity, but an end user happens to be in an area of reduced radio communications quality, an otherwise uncongested radio channel should proceed with the data content delivery as quickly as the network will allow, because the transfer session will likely not affect cross traffic (even when proceeding at a maximum transfer rate).
There may also be scenarios where multiple media content deliveries for large media content files are concurrently being transferred on the same, shared communications channel. In these scenarios it would be advantageous to be able detect that congestion is arising from multiple media content delivery sessions, as opposed to unrelated cross traffic. This could facilitate real time decision making as to whether or not to reschedule or alter data content deliveries in such a way that would avoid slowing data transfer rates below an aggregate rate that completely utilizes a communications channel. By knowing what data content is being transferred, channel resource utilization can be maximized at all times. This is so, because media content deliveries are generally considered to be lower priority data transfer tasks compared to other, less burdensome types of data communications, such as voice communications.
Another feature of large data content file delivery is that data transfers can occupy significant periods of time and they may be scheduled to start at random intervals. Both of these features may result in frequent communications between user equipment and the network for communications of both control/signaling information and actual media content data. To facilitate efficient deliveries, it would be beneficial if these frequent communications could be coordinated to minimally impact the resident resource (e.g., battery power, processor usage, available memory, etc.) consumption at a user equipment. This could reduce the effect that the media content transfer would have on the user equipment during periods when one or more resident device resources was in a state of resource exhaustion (e.g., low battery power, an overburdened processor, of reduced free memory, etc.). By selectively coordinating data content deliveries towards periods when resident device resources are not in a reduced state, more important processes supported by the user equipment (e.g., voice communications, texting, web browsing, etc.) could be prioritized, until a time when sufficient resources become available (e.g., when a user equipment is plugged into a local power supply) for lower priority media content delivery tasks.
Accordingly, it would be desirable to have robust new systems and methods that could align data transfer sessions for burdensome media content away from peak periods of network use (periods associated with high levels of network traffic), towards periods of surplus network capacity, by accurately detecting a state of network congestion that is distinguished from a state of reduced radio communications quality. It would further be advantageous if these systems and methods could operate by automatically detecting, coordinating, and delivering burdensome media content to one or more end receiving device(s), such that a typical user would be unaware of how these underlying data transfer rate optimization/throttling processes functioned. As a result, an average network user's QOE should improve, while the underlying processes facilitating the improvement would remain transparent. It would further be desirable if these systems and methods could discern between congestion created by cross traffic as opposed to congestion created by other media content transfers occurring on the same communications channel. This would allow a service provider to fully utilize its network channel resources at all times and to prioritize some data communications processes over others (e.g., media content transfers are typically lower priority data type transfers). It would also be helpful if these systems and methods facilitated real time monitoring of user equipment resources, such that when local resources (e.g., battery power, processor usage, available memory, etc.) were in a state or resource exhaustion, a media content delivery could be slowed or halted until the resources were replenished or became available at the user equipment. These real time solutions could be used to mitigate situations where large media content deliveries would otherwise degrade or impair communications on a network communications channel for a network's collective users.